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A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan
A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan
Graduate Student Theses, Dissertations, & Professional Papers
Surrogate modeling is a new and expanding field in the world of deep learning, providing a computationally inexpensive way to approximate results from computationally demanding high-fidelity simulations. Ice sheet modeling is one of these computationally expensive models, the model used in this study currently requires between 10 and 20 minutes to complete one simulation. While this process is adequate for certain applications, the ability to use sampling approaches to perform statistical inference becomes infeasible. This issue can be overcome by using a surrogate model to approximate the ice sheet model, bringing the time to produce output down to a tenth …
Trading Financial Instruments Like A Video Game: Searching For Profit Using Deep Reinforcement Learning., Sebastian Coombs
Trading Financial Instruments Like A Video Game: Searching For Profit Using Deep Reinforcement Learning., Sebastian Coombs
Graduate Student Theses, Dissertations, & Professional Papers
Buying and selling Stocks, Foreign Currencies (FOREX), Commodities, and Cryptocurrencies have been a source of wealth generation, and more often, wealth loss for many brave enough to enter the financial markets. In this paper, the author builds on the work of Williams, J. 2022 and develops an agent-based method to solve this wealth generation problem with the use of neural networks. The author points out some assumptions made by Williams, J. 2022 that were sound in theory, but made the implementation of the algorithm presented in their paper diverge from the theory. The author proposes a fundamentally different algorithmic method, …